MOORA-Driven Decision Making to Select the Optimal Specimen of Organic CMCs

Bone grafting or bone implant is a typical procedure in surgery in which a missing or broken bone is replaced in order to treat bone fractures that pose a significant health risk to the patients. Several research works have been carried out in the past few years regarding various composite materials used in bone implants, their fabrication methods, and evaluation of their physical, mechanical, chemical, and thermal properties. The use of ceramic powders and ceramic-based composites in biomedical applications are steadily increasing over years mainly due to their advantages like high compressive strength, excellent hardness, etc. In this research work, organic ceramic matrix composites with varying proportions of conch shell and sea sponge are fabricated using powder metallurgy technique and their physicomechanical properties such as density, porosity, water absorption, and micro-hardness are evaluated. Finally, optimization of process parameters is done using multi-objective optimization based on ratio analysis (MOORA) to select the best possible specimen of CMCs.

Optimization of Surface Roughness in Centreless Grinding Process Based on Taguchi Method

This chapter aims to optimize centreless grinding conditions using the Taguchi method for minimizing surface roughness. The grinding operation has been performed according to the L9 orthogonal array in a centreless grinding process. The centreless grinding experiments are carried out on the crane-hook pin of C40 steel. The analysis of variance (ANOVA) and computation of signal to noise (S/N) ratio are adopted to determine the influence of grinding parameters (depth of cut [µm], regulating wheel speed [rpm], and coolant valve opening) on surface roughness. The depth of cut (µm) is found to be the most significant among the grinding parameters on the surface roughness. The signal to noise (S/N) ratio was calculated based on smaller the best criteria. The lower level of depth of cut, medium level of regulating wheel speed, and higher-level coolant valve opening is found to be optimal grinding condition according to the mean response and signal to noise (S/N) ratio.

Progress in Optimization of Physical Vapor Deposition of Thin Films

In this chapter, the current state of the art in optimization of thin film deposition processes is discussed. Based on the reliable and credible published results, the study aims to identify the applications of various optimization techniques in the thin film deposition processes, with emphasis on physical deposition methods. These methods are chosen due to their attractive attributes over chemical deposition techniques for thin film manufacturing. The study identifies the critical parameters and factors, which are significant in designing of the optimization algorithms based on the specific deposition methods. Based on the specific optimization studies, the chapter provides general trends, optimization evaluation criteria, and input-output parameter relationships on thin film deposition. Research gaps and directions for future studies on optimization of physical vapor deposition methods for thin film manufacturing are provided.

Solution for Multi-Objective Single Row Facility Layout Problem Using PSO Algorithm

An optimized facility layout design helps to ensure a high level of machine usability along with minimum cost and good performance. However, facility layout is a multi-objective design optimization problem, and as such, it is difficult to solve. This original research work aims to design an optimal linear machine sequence using particle swarm optimization algorithm that minimizes the following: the total investment cost of machines, the total number of machines in the final sequence, the total flow time of the products, and the total flow distance of the products. The effectiveness of the proposed algorithm is demonstrated with a reasonable number of problems. Maximum of 19.1% reduction in total flow distance of products, 12.8% reduction in total investment cost of machines, 28.4% reduction in total flow time of products, and reduction of two numbers of machines in the layout are achieved by proposed method compared with the previous approaches.

Machine Learning-Based Predictive Modelling of Dry Electric Discharge Machining Process

The conventional methods like linear or polynomial regression, despite their overwhelming accuracy on training data, often fail to achieve the same accuracy on independent test data. In this research, a comparative study of three different machine learning techniques (linear regression, random forest regression, and AdaBoost) is carried out to build predictive models for dry electric discharge machining process. Six different process parameters namely voltage gap, discharge current, pulse-on-time, duty factor, air inlet pressure, and spindle speed are considered to predict the material removal rate. Statistical tests on independent test data show that despite linear regression's considerable accuracy on training data, it fails to achieve the same on independent test data. Random forest regression is seen to have the best performance among the three predictive models.

Data-Driven Genetic Programming-Based Symbolic Regression Metamodels for EDM Process

In this research, a data-driven approach to metamodeling of manufacturing/machining processes is developed. Instead of the conventionally used second-order polynomial regression metamodels, a non-predefined form-free approach is discussed. The highly adaptive metamodeling strategy, called symbolic regression, is carried out by using genetic programming. A central composite design based experimental dataset on electric discharge machining is used as the training and the testing data. Four different process parameters namely (voltage, pulse on time, pulse off time, and current) are used as the independent parameters to quantify three different responses (material removal rate, electrode wear rate, and surface roughness). The performance of the metamodels are evaluated by using various statistical metrics like R2, MAE, MSE. The performance of the metamodels on the training and testing data is found to be adequate for all the responses.

TOPSIS-Based Selection of Optimal Proportion Among Different Combinations of Hybrid AMCs

In this modern world, composites are used in almost all fields due to their attractive mechanical and technological properties. They are believed to be fast replacing metal alloys thanks to their adaptability, flexibility, formability, and machinability. The present study deals with the comparative evaluation of mechanical properties between various aluminium alloy composites reinforced with boron carbide and rice husk ash at different proportions and their optimization through ranking of alternatives using TOPSIS. The composite specimens are fabricated by a liquid metallurgy technique called stir casting. The sample specimens are prepared by varying the percentage of reinforcements as per volume-based ratio with respect to the aluminium alloy Al 6061. The evaluation of mechanical properties indicates the improvement in tensile strength, hardness, impact energy, and corrosion resistance for different composite combinations compared to that of individual alloy. Finally, the best possible combination is identified among the given set of various proportions by optimization using TOPSIS.

Evaluation of Optimum Parameters for Casting of Birla Lance Pipes

Defects associated with casting of pipes are often a main concern for the industry. In this chapter, a Taguchi analysis is carried out to understand the effect of three process parameter pouring temperature (°C), die spinning speed (rpm), and coolant flow time (mins) on the casting defect of pipes. The defect is defined in this work as the difference between the desired thickness of the pipe and the minimum actual (experimentally) achieved. A L9 orthogonal array is designed to carry out the experiments. Based on the S/N ratio analysis and ANOVA, it is seen that the die spinning speed plays the most critical role in defect of the pipes. As per the conducted experiments and Taguchi analysis, pouring temperature is seen to have the lest influence on the defects.

Optimization of Drilling Parameters for Composite Laminate Using Genetic Algorithm

Composite materials are preferred mostly in recent times due to their durability and ample space of applicability. Drilling is also an essential process in manufacturing, and it is frequently done to assemble products. Delamination due to drilling of the CFRP composite is considered as the primary concern in the manufacturing and assembly process. In this chapter, the empirical model for thrust force, torque, entry-delamination factor, exit-delamination factor, and eccentricity of drilling of CFRP composite is developed based on the extensive experiment. Response surface methodology is accounted to formulate a mathematical modal considering thrust force, torque, entry and exit-delamination factor, and eccentricity as response parameter and spindle speed, feed rate, and point angle as a process parameter. ANOVA is performed to check the statistical significance of the mathematical model. GA is employed to trace the optimum values of three process parameters to minimize the five response parameters. The Pareto front curve for various combinations of process parameters is also examined.

Parametric Optimization of Electrochemical Discharge Machining Using Particle Swarm Optimization Algorithm

It is very important to select the optimal parametric values for various non-traditional machining processes (NTM) for improving their performance. The performance measures of NTM processes include material removal rate (MRR), radial overcut (ROC), heat affected zone (HAZ), etc. In this chapter, particle swarm optimization has been used to find out the optimal parametric settings for electrochemical discharge machining (ECDM) to improve its performance measure. Both single-objective as well as multi-objective optimization has been performed and the results have been compared with those obtained by other researchers.